在基于EMG的模式识别（EMG-PR）中，基于深度学习的技术因其从大型数据集中提取判别特征的自我调节能力而变得更加突出。此外，传统的基于机器学习的方法的性能显示出对一定数量的类进行分类的限制，并且在一段时间内会下降。在本文中，一个准确的，鲁棒的，快速的卷积神经网络为基础的框架，肌电信号模式识别。为了评估所提出的系统的性能，五个公开和基准数据集的上肢活动。该数据集包含49至52个上肢运动（NinaPro DB 1、NinaPro DB 2和NinaPro DB 3）、力变化数据以及完整和截肢受试者手臂位置变化数据。在测试和验证过程中观察到的分类准确率为91.11%（53类），89.45%（49类），81.67%（49类截肢者），95.67%（6类力变化）和99.11%（8类手臂位置变化）。所提出的系统的性能进行了比较，在文献中的最先进的技术。研究结果表明，分类精度和时间复杂度有显着改善。Keras是TensorFlow用于构建深度学习模型的高级API，用于信号预处理和基于深度学习的算法。建议的方法在Intel 3.5GHz Core i7，第七代CPU和8 GB DDR4 RAM上运行。
英文摘要：
In EMG based pattern recognition (EMG-PR), deep learning-based techniques have become more prominent for their self-regulating capability to extract discriminant features from large data-sets. Moreover, the performance of traditional machine learning-based methods show limitation to categorize over a certain number of classes and degrades over a period of time. In this paper, an accurate, robust, and fast convolutional neural network-based framework for EMG pattern identification is presented. To assess the performance of the proposed system, five publicly available and benchmark data-sets of upper limb activities were used. This data-set contains 49 to 52 upper limb motions (NinaPro DB1, NinaPro DB2, and NinaPro DB3), Data with force variation, and data with arm position variation for intact and amputated subjects. The classification accuracies of 91.11% (53 classes), 89.45% (49 classes), 81.67% (49 classes of amputees), 95.67% (6 classes with force variation), and 99.11% (8 classes with arm position variation) have been observed during the testing and validation. The performance of the proposed system is compared with the state of art techniques in the literature. The findings demonstrate that classification accuracy and time complexity have improved significantly. Keras, TensorFlow's high-level API for constructing deep learning models, was used for signal pre-processing and deep-learning-based algorithms. The suggested method was run on an Intel 3.5GHz Core i7, 7th Gen CPU with 8GB DDR4 RAM.
Cascade of one-class classifier ensemble and dynamic naive Bayes classifier applied to the myoelectric-based upper limb prosthesis control with contaminated channels detection
Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）
Improving the clinical utility of lower-limb surface electromyography (sEMG) by quantifying and correcting for location changes in inter-session recordings
School of Biomedical Engineering, University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；Centre for Aging SMART, University of British Columbia（不列颠哥伦比亚大学老龄化SMART中心）；Focal Lines Technologies（Focal Lines科技有限公司）
Evaluation of Embedded Platforms for Lower Limb Prosthesis with Visual Sensing Capabilities
下肢假肢可以从能够应用计算机视觉技术的嵌入式系统中受益，以增强自主控制和智能决策的上下文感知。为了填补目前文献中采用计算机视觉方法的假肢自主系统的空白，我们评估了两个现成平台的性能，Jetson TX2和Raspberry Pi 3，通过评估它们的CPU负载，内存使用，运行时间和分类精度不同的图像大小和广泛的计算机视觉算法。我们利用一个数据集，我们收集的地形识别使用安装在腿上的摄像头，这将使下肢假肢的上下文意识的图像。我们表明，合理的大图像和适当的帧选择方法，它是可以识别的地形，一个主题是要踩，重建表面和获得其倾斜的可能性。这是一个拟议的系统的一部分，配备了一个嵌入式相机和惯性测量单元，以识别不同类型的地形，并估计在受试者面前的表面的斜率。
Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism
Concordia University（康考迪亚大学）；Concordia Institute for Information System Engineering（康考迪亚信息系统工程研究院）；Imperial College London（帝国理工学院）；New York University（纽约大学）
